In early 2020, the British company called Cambridge Quantum Computing announced the introduction of a “meaning-aware” Quantum NLP model, i.e. a mathematical theory able to splice the semantic information of words with the syntactic structure of a sentence [1]}. The bold claim is based on the observation that syntactic structures such as Noam Chomsky’s Context-free grammars or Categorical Compositional Distributional (DisCoCat) can be formulated in the framework of quantum physics [1]}. While the concept of a Hilbert space for NLP may seem far-fetched at first, an intuitive explanation goes as follows: NLP-as-we-know-it is rooted in classical statistics (for example, word embeddings are vectors in a \(\mathbb {R}^d\)  space [3]}), but classical statistics itself has its own limits. The field of physics called quantum mechanics is described by the mathematics of quantum statistics, which extends classical statistics by representing objects with matrices of complex numbers. Even if we are not claiming that words are made of particles, it does happen by accident that the kind of statistics needed to describe human language is substantially the same of quantum physics. Coincidentally, we are also entering a time in which rudimentary and still quite noisy devices [4]} able to carry out computations based on qubits rather than digital bits of information are becoming more and more accessible to the wider public (including small businesses, start-ups and individuals). Thus, it makes sense in this decade to explore the possibility that quantum computing may give a boost to natural language processing.
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